1. Yang, Z., Xu, D., Zhang, Y., Chen, K., Wang, X., Yang, Xu., Pang, W. and Yuan, Y.†, 2026. Do Vision and Text Cues Exhibit Evidential Coupling? UFO: A Benchmark for Compositional Multimodal Reasoning in Unified Models. accepted by the International Conference on Machine Learning 2026.
    ICML
  2. Yang, Z., Yang, Z., Zhan, S., Yue, T., Pang, W. and Yuan, Y.†, 2026. SVAgent: Storyline-guided Long Video Understanding via Cross-modal Multi-agent Collaboration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 24062-24072.
    CVPR
  3. Yang, Z., Pang, W. and Yuan, Y.†, 2026. XR: Cross-Modal Agents for Composed Image Retrieval. In: Proceedings of the ACM Web Conference 2026, pp. 2071-2082.
    WWW
  4. Yang, Z., Yuan, Y.*, Jiang, X., An, B. and Pang, W., 2026. InEx: Hallucination Mitigation via Introspection and Cross-Modal Multi-Agent Collaboration. In: Proceedings of the AAAI Conference on Artificial Intelligence, 40(35), pp. 29829-29837.
    AAAI
  5. Yang, Z., Xu, D., Pang, W. and Yuan, Y.†, 2025. Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models. accepted by Transactions on Machine Learning Research.
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  6. Yang, Z., Song, J., Luo, Z., Yang, Z., Xu, Y., Lan, J., Zhang, Y., Pang, W., Song, S. and Yuan, Y.†, 2025. ReChar: Revitalising Characters with Structure Preserved and User-Specified Aesthetic Enhancements. In: Proceedings of the SIGGRAPH Asia 2025 Technical Communications, pp. 1-5.
    SIGGRAPH Asia
  7. Liu, Z., Li, Y., Xu, Y., Wang, Y., Yuan, Y. and Yang, Z., 2025. Evaluating Text Generation Quality Using Spectral Distances of Surprisal. In: Findings of the Association for Computational Linguistics: EMNLP 2025, pp. 2444-2463.
    EMNLP
  8. Yang, Z., Song, J., Song, S., Pang, W. and Yuan, Y.†, 2025. MERMAID: Multi-perspective Self-reflective Agents with Generative Augmentation for Emotion Recognition. In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 24650-24666.
    EMNLP
  9. Yuan, Y.*, Chen, K.*, Rizvi, M., Baillie, L. and Pang, W., 2025. Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness. In: 2025 International Joint Conference on Neural Networks, pp. 1-10. IEEE. (Oral)
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  10. Song, J., Yuan, Y.*, Chang, K., Xu, B., Xuan, J. and Pang, W., 2024. Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter Optimisation. Energy and AI, 18, 100433.
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  11. Yuan, Y., Wang, W., Li, X., Chen, K., Yonghan, Z. and Pang, W., 2024. Evolving Molecular Graph Neural Networks with Hierarchical Evaluation Strategy. In: Proceedings of the Genetic and Evolutionary Computation Conference 2024.
    GECCO
  12. Song, J.*, Yuan, Y.* and Pang, W., 2024. SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm. In: Proceedings of the Genetic and Evolutionary Computation Conference Workshop 2024.
    GECCO
  13. Yuan, Y.*, Yang, Z.*, Xu, Y.*, Zhan, S., Bai, H. and Chen, K., 2023. FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy. In: Advances in Neural Information Processing Systems 36.
    NeurIPS
  14. Hasan, S. and Yuan, Y., 2023. Minority Ethnic Vulnerabilities in the Use of Digital Housing Services Across Age Groups. European Network for Housing Research.
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  15. Yuan, Y., Wang, W. and Pang, W., 2021. Which hyperparameters to optimise? An investigation of evolutionary hyperparameter optimisation in graph neural network for molecular property prediction. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1403-1404.
    GECCO
  16. Yuan, Y., Wang, W. and Pang, W., 2021. A genetic algorithm with tree-structured mutation for hyperparameter optimisation of graph neural networks. In: 2021 IEEE Congress on Evolutionary Computation, pp. 482-489. IEEE.
    CEC
  17. Yuan, Y., Wang, W. and Pang, W., 2021. A systematic comparison study on hyperparameter optimisation of graph neural networks for molecular property prediction. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 386-394.
    GECCO
  18. Wang, W., Moreau, N.G., Yuan, Y., Race, P.R. and Pang, W., 2019. Towards machine learning approaches for predicting the self-healing efficiency of materials. Computational Materials Science, 168, pp.180-187.
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